Academic Journal
Pharmacometrics in the Age of Large Language Models: A Vision of the Future.
| Τίτλος: | Pharmacometrics in the Age of Large Language Models: A Vision of the Future. |
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| Συγγραφείς: | Tosca, Elena Maria, Aiello, Ludovica, De Carlo, Alessandro, Magni, Paolo |
| Πηγή: | Pharmaceutics; Oct2025, Vol. 17 Issue 10, p1274, 26p |
| Θεματικοί όροι: | Language models, Artificial intelligence, Prediction models, Digital twin, Pharmacology, Electronic data processing, Drug development |
| Περίληψη: | Background: Large Language Models (LLMs) have driven significant advances in artificial intelligence (AI), with transformative applications across numerous scientific fields, including biomedical research and drug development. However, despite growing interest in adjacent domains, their adoption in pharmacometrics, a discipline central to model-informed drug development (MIDD), remains limited. This study aims to systematically explore the potential role of LLMs across the pharmacometrics workflow, from data processing to model development and reporting. Methods: We conducted a comprehensive literature review to identify documented applications of LLMs in pharmacometrics. We also analyzed relevant use cases from related scientific domains and structured these insights into a conceptual framework outlining potential pharmacometrics tasks that could benefit from LLMs. Results: Our analysis revealed that studies reporting LLMs in pharmacometrics are few and mainly limited to code generation in general-purpose programming languages. Nonetheless, broader applications are theoretically plausible and technically feasible, including information retrieval and synthesis, data collection and formatting, model coding, PK/PD model development, support to PBPK and QSP modeling, report writing and pharmacometrics education. We also discussed visionary applications such as LLM-enabled predictive modeling and digital twins. However, challenges such as hallucinations, lack of reproducibility, and the underrepresentation of pharmacometrics data in training corpora limit the actual applicability. Conclusions: LLMs are unlikely to replace mechanistic pharmacometrics models but hold great potential as assistive tools. Realizing this potential will require domain-specific fine-tuning, retrieval-augmented strategies, and rigorous validation. A hybrid future, integrating human expertise, traditional modeling, and AI, could define the next frontier for innovation in MIDD. [ABSTRACT FROM AUTHOR] |
| Copyright of Pharmaceutics is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Biomedical Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Pharmacometrics in the Age of Large Language Models: A Vision of the Future. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tosca%2C+Elena+Maria%22">Tosca, Elena Maria</searchLink><br /><searchLink fieldCode="AR" term="%22Aiello%2C+Ludovica%22">Aiello, Ludovica</searchLink><br /><searchLink fieldCode="AR" term="%22De+Carlo%2C+Alessandro%22">De Carlo, Alessandro</searchLink><br /><searchLink fieldCode="AR" term="%22Magni%2C+Paolo%22">Magni, Paolo</searchLink> – Name: TitleSource Label: Source Group: Src Data: Pharmaceutics; Oct2025, Vol. 17 Issue 10, p1274, 26p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmacology%22">Pharmacology</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+development%22">Drug development</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Large Language Models (LLMs) have driven significant advances in artificial intelligence (AI), with transformative applications across numerous scientific fields, including biomedical research and drug development. However, despite growing interest in adjacent domains, their adoption in pharmacometrics, a discipline central to model-informed drug development (MIDD), remains limited. This study aims to systematically explore the potential role of LLMs across the pharmacometrics workflow, from data processing to model development and reporting. Methods: We conducted a comprehensive literature review to identify documented applications of LLMs in pharmacometrics. We also analyzed relevant use cases from related scientific domains and structured these insights into a conceptual framework outlining potential pharmacometrics tasks that could benefit from LLMs. Results: Our analysis revealed that studies reporting LLMs in pharmacometrics are few and mainly limited to code generation in general-purpose programming languages. Nonetheless, broader applications are theoretically plausible and technically feasible, including information retrieval and synthesis, data collection and formatting, model coding, PK/PD model development, support to PBPK and QSP modeling, report writing and pharmacometrics education. We also discussed visionary applications such as LLM-enabled predictive modeling and digital twins. However, challenges such as hallucinations, lack of reproducibility, and the underrepresentation of pharmacometrics data in training corpora limit the actual applicability. Conclusions: LLMs are unlikely to replace mechanistic pharmacometrics models but hold great potential as assistive tools. Realizing this potential will require domain-specific fine-tuning, retrieval-augmented strategies, and rigorous validation. A hybrid future, integrating human expertise, traditional modeling, and AI, could define the next frontier for innovation in MIDD. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Pharmaceutics is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/pharmaceutics17101274 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1274 Subjects: – SubjectFull: Language models Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Digital twin Type: general – SubjectFull: Pharmacology Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Drug development Type: general Titles: – TitleFull: Pharmacometrics in the Age of Large Language Models: A Vision of the Future. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tosca, Elena Maria – PersonEntity: Name: NameFull: Aiello, Ludovica – PersonEntity: Name: NameFull: De Carlo, Alessandro – PersonEntity: Name: NameFull: Magni, Paolo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19994923 Numbering: – Type: volume Value: 17 – Type: issue Value: 10 Titles: – TitleFull: Pharmaceutics Type: main |
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